Public transportation services are ubiquitous in the world’s cities, whether more traditional taxis or newer mobile-powered startups like Uber or Lyft. While using those means of public transportation is environmentally friendly, it’s not a perfect system. It might help reduce the number of cars on the road, but it does not necessarily reduce the number of miles driven, and it definitely does not reduce the amount of pollution generated by gas engines. Much has been written about the “smart cities” of tomorrow; infrastructure that relies upon digital information and communication technologies to make city living more efficient, more ecologically aware, and healthier. One of the main ways in which that could be accomplished is through innovations designed to reduce traffic.

Vehicular traffic is, according to MIT Senseable City Lab researcher Paolo Santi and colleagues, “one of the greatest challenges facing cities all over the world.” For the 83 largest urban areas in the United States, traffic results in a waste of time and fuel valued at some $60 billion dollars. The World Health Organization estimates that more than one million deaths each year are attributed to air pollution, of which a large proportion is caused by vehicular traffic. In addition, traffic results in human and wildlife fatalities through road accidents, and in economic losses from missed business-related activities. Santi’s team wondered whether public transportation services such as taxis could be modified to reduce traffic by promoting the sharing of cars or minivans.

The two main socio-psychological barriers to carpooling are the extra time that carpooling takes and the loss of privacy. During the oil crisis of the 1970s, carpooling was embraced because the economic realities outweighed those barriers to ride sharing. Santi believes that those obstacles can be overcome once again by the implementation of “sharing economies.” The idea makes intuitive sense, but stakeholders have been reluctant to implement such a system in the absence of quantifiable evidence. Now we have that evidence.

The researchers started with a massive dataset: every trip taken by each of New York City’s 13,586 registered taxis that either started or ended in Manhattan in the year 2011. That gave them more than 150 million taxi trips. Each data point included the vehicle ID number, the GPS coordinates of the pickup and drop-off locations, and the travel time.

Pickups and dropoffs of 150 million taxi trips over one year in New York City. Image courtesy of HubCab, MIT Senseable City Lab.

By passing that data through a graph-based mathematical model, the researchers identified opportunities for trip sharing without re-routing trips that had already started. The system that the researchers designed worked such that sharing options would have to be identified within one minute of the ride request. If no viable sharing options existed, then that request would initiate a new ride. That way, already-existing trips would not have to be re-routed; they would only pick up new passengers if the additional trip’s pickup and drop-off points were “on the way” to the original destination. By implementing such a program, the researchers estimate that transportation within Manhattan would become 40% more efficient.

In addition, Santi writes that the system is scalable to smaller cities with far fewer taxis. Each day, taxis provide an average of 400,000 trips. But peak share-ability is already achieved at just 25% of that, or 100,000 daily trips. That means that taxi-sharing systems would be effective in cities with just one quarter of the taxi demand of New York City.

The next step, the researchers say, is to empirically validate their assumption that the carpooling programs do indeed apply outside of New York City. The algorithms could be even more fine-tuned by incorporating data on how long passengers take to find a taxi and how and when passengers travel in groups versus alone. Finally, they hope to design processes to equitably split the cost of the ride among the passengers as well as to fairly distribute the economic benefits of ride sharing between the drivers and the passengers. – Jason G. Goldman | 03 September 2014

Lots of holes in this one. Dataset didn’t have the number of passengers so the number of possible shares will be less. 40% less traffic, or 40% less taxi traffic? What about the global loss of income to taxi drivers? Great when the weather is bad and there is a shortage of taxis anyway. Fairly easy to implement. People in Nyc are used to giving up privacy but not so much in other cities.